ADVANCING MOVIE RECOMMENDATIONS WITH NAIVE BAYES AND NLP ALGORITHMS

Abstract

Nowadays, movie recommendation systems are one of the best tools to improve the user experience on streaming platforms in the digital age. This study aims to design a movie recommender system based on Naive Bayes algorithm and NLP in data mining. This system predicts user preferences to support movie suggestions, considering both the historical viewing of the user and a textual representation of movie descriptions and reviews. The integration of such services makes the system to properly categorize the user interest in different genres with the help of Naive Bayes algorithm, NLP techniques make to extract and analyse the significant features from the textual data. By combining these methodologies, diversified picture can be achieved, they could be more informative in terms of what users like and be more useful for predicting preferences more individually accurate as well. Experiments show that the hybrid fat approach outperforms traditional and achieves increased recommendation accuracy. It demonstrates the prospective utilization of machine learning and NLP in recommendation system which can be used as a base for future work on personalization content delivery.